{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3C. Data Modeling: Clustering\n",
    "<hr>\n",
    "\n",
    "We will now assess the results of the zipcode -> neighborhood clustering conversion, running the same models as in ```3A. Data Modeling: Airbnb Listings``` and comparing the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cmx\n",
    "import matplotlib.colors as colors\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression as Lin_Reg\n",
    "from sklearn.linear_model import Ridge as Ridge_Reg\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.linear_model import Lasso as Lasso_Reg\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import pylab\n",
    "import scipy.stats as stats\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# helper function for plotting residual plots\n",
    "def plot_residual(ax1, ax2, ax3, y_pred, y_real, line_label, title):\n",
    "    ax1.scatter(y_pred, \n",
    "                y_real, \n",
    "                color='blue',\n",
    "                alpha=0.6,\n",
    "                label=line_label)\n",
    "    ax1.set_xlabel('Predicted Y') \n",
    "    ax1.set_ylabel('Real Y')\n",
    "    ax1.legend(loc='best')\n",
    "    ax1.set_title(title)\n",
    "\n",
    "    ax2.scatter(y_pred,\n",
    "                y_real - y_pred, \n",
    "                color='green',\n",
    "                marker='x',\n",
    "                alpha=0.6,\n",
    "                label='Residual')\n",
    "    ax2.set_xlabel('Predicted Y')\n",
    "    ax2.set_ylabel('Residual')\n",
    "    \n",
    "    ax2.axhline(y=0, color='black', linewidth=2.0, alpha=0.7, label='y=0')\n",
    "\n",
    "    ax2.legend(loc='best')\n",
    "    ax2.set_title('Residual Plot')\n",
    "    \n",
    "    ax3.hist(y_real - y_pred, bins=30, color='green', alpha=0.7)\n",
    "    ax3.set_title('Histogram of residual values')\n",
    "    \n",
    "    return ax1, ax2, ax3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class model:\n",
    "    \n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "        self.x_train = None\n",
    "        self.y_train = None\n",
    "        self.x_test = None\n",
    "        self.y_test = None\n",
    "        self.y_pred_train = None\n",
    "        self.y_pred_test = None\n",
    "        self.train_score = None\n",
    "        self.test_score = None\n",
    "        self.train_score_log = None\n",
    "        self.test_score_log = None\n",
    "    \n",
    "    def data_split(self, x, y, test_size):\n",
    "        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(x, y, test_size=test_size)\n",
    "    \n",
    "    def score_reg(self):\n",
    "        return self.train_score, self.test_score\n",
    "    \n",
    "    def score_log(self):\n",
    "        self.train_score_log = metrics.r2_score(np.exp(self.y_train), np.exp(self.y_pred_train))\n",
    "        self.test_score_log = metrics.r2_score(np.exp(self.y_test), np.exp(self.y_pred_test))\n",
    "        return self.train_score_log, self.test_score_log\n",
    "    \n",
    "    def data_frame_convert(self):\n",
    "        df_train = pd.DataFrame({'y_pred': self.y_pred_train, 'y_real': self.y_train})\n",
    "        df_test = pd.DataFrame({'y_pred_test': self.y_pred_test, 'y_real_test': self.y_test})\n",
    "        return self.train_score, self.test_score, df_train, df_test\n",
    "\n",
    "    def data_frame_convert_log(self):\n",
    "        df_train = pd.DataFrame({'y_pred': np.exp(self.y_pred_train), 'y_real': np.exp(self.y_train)})\n",
    "        df_test = pd.DataFrame({'y_pred_test': np.exp(self.y_pred_test), 'y_real_test': np.exp(self.y_test)})\n",
    "        return self.train_score_log, self.test_score_log, df_train, df_test\n",
    "    \n",
    "    def fit_model(self, x, y, test_size):\n",
    "        self.data_split(x, y, test_size)\n",
    "        self.model = self.model.fit(self.x_train, self.y_train)\n",
    "        self.train_score = self.model.score(self.x_train, self.y_train)\n",
    "        self.test_score = self.model.score(self.x_test, self.y_test)\n",
    "        self.y_pred_train = self.model.predict(self.x_train)\n",
    "        self.y_pred_test = self.model.predict(self.x_test)\n",
    "    \n",
    "def model_iterations(n, x, y, model_arg, log_bool=False):\n",
    "    new_model = model(model_arg)\n",
    "    training_scores = [None]*n\n",
    "    testing_scores = [None]*n\n",
    "\n",
    "    for i in range(n):\n",
    "        new_model.fit_model(x, y, 0.3)\n",
    "        training_scores[i], testing_scores[i] = new_model.score_reg() if not log_bool else new_model.score_log()\n",
    "\n",
    "    print 'Mean Train Score:', np.mean(training_scores)\n",
    "    print 'Mean Test Score:', np.mean(testing_scores)\n",
    "    return new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in neighborhood dataset\n",
    "data = pd.read_csv('../datasets/listings_neighborhood_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# split into x and y (note that we do not include id and host_id as predictors)\n",
    "x = data.iloc[:, 2:-2]\n",
    "y = data.iloc[:, -2]\n",
    "y_log = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge & Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression with Untransformed Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Train Score: 0.317389383417\n",
      "Mean Test Score: 0.287039215886\n"
     ]
    }
   ],
   "source": [
    "reg_params = 10.**np.linspace(-10, 5, 10)\n",
    "RidgeCV_model = model_iterations(1, x, y, RidgeCV(alphas=reg_params, fit_intercept=True, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XXvorHnjgIXbYYQcKC3ty0kmnUl5ezv3330uPHgWsXbuWzz9fRZcuXfnXv0rY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a1kRCVqg5CbJw+ytcNe5L+Fw+Ur2pYNv4XL4uLTzqNt0U5Ezp8pEhQhxDbDGxNKASqKZ18iIN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\nQoiE9/8B0nyJdu3CucQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b847210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the residuals of a ridge regression model\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "ridge_data_vals = RidgeCV_model.data_frame_convert()\n",
    "plot_residual(axes[0][0], axes[0][1], axes[0][2], ridge_data_vals[2]['y_pred'], ridge_data_vals[2]['y_real'], 'Ridge Regression: {}'.format(ridge_data_vals[0]), 'Scatter Plot: Predicted vs. Real Y')\n",
    "plot_residual(axes[1][0], axes[1][1], axes[1][2], ridge_data_vals[3]['y_pred_test'], ridge_data_vals[3]['y_real_test'], 'Ridge Regression: {}'.format(ridge_data_vals[1]), 'Residual Plot for Test Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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csqXYIYiIlL1Bk0juuQeamjqpqorT1NRZcgPtIiLlatBM/502DaZM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\nREREcqJEIiIiOVEiERGRnCiRiIhITv4/vmwxTJE5JVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12405ff50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# qq plot for the train residuals\n",
    "stats.probplot(ridge_data_vals[2]['y_pred'] - ridge_data_vals[2]['y_real'], dist=\"norm\", plot=pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression with Log-Transformed Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Train Score: 0.355490988957\n",
      "Mean Test Score: 0.361796683246\n"
     ]
    }
   ],
   "source": [
    "reg_params = 10.**np.linspace(-10, 5, 10)\n",
    "RidgeCV_model = model_iterations(5, x, y_log, RidgeCV(alphas=reg_params, fit_intercept=True, cv=5), True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z77//H/bZpzOXXnph+euxWIxBg8JIuzVr1gAhaJ6dvfF3iIiIyJZSgEMahAMP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119320490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the residuals of a ridge regression model\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "ridge_data_vals = RidgeCV_model.data_frame_convert_log()\n",
    "plot_residual(axes[0][0], axes[0][1], axes[0][2], ridge_data_vals[2]['y_pred'], ridge_data_vals[2]['y_real'], 'Ridge Regression: {}'.format(ridge_data_vals[0]), 'Scatter Plot: Predicted vs. Real Y')\n",
    "plot_residual(axes[1][0], axes[1][1], axes[1][2], ridge_data_vals[3]['y_pred_test'], ridge_data_vals[3]['y_real_test'], 'Ridge Regression: {}'.format(ridge_data_vals[1]), 'Residual Plot for Test Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c2MXtt+c3EV+NogxtjU+6GwPeD/RsbVjhbQQak+5HSiJNTY25qpRdNcQI6eNsbIS2quqp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\nFyUSERHJixKJiIjk5f8DFzMotzGW22cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1262d1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# qq plot for the train residuals\n",
    "stats.probplot(ridge_data_vals[2]['y_pred'] - ridge_data_vals[2]['y_real'], dist=\"norm\", plot=pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso Regression with Untransformed Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Train Score: 0.305523648264\n",
      "Mean Test Score: 0.313385158838\n"
     ]
    }
   ],
   "source": [
    "reg_params = 10.**np.linspace(-10, 5, 10)\n",
    "LassoCV_model = model_iterations(5, x, y, LassoCV(alphas=reg_params, fit_intercept=True, cv=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso Regression with Log-Transformed Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Train Score: 0.362542038651\n",
      "Mean Test Score: 0.340433970597\n"
     ]
    }
   ],
   "source": [
    "reg_params = 10.**np.linspace(-10, 5, 10)\n",
    "LassoCV_model = model_iterations(5, x, y_log, LassoCV(alphas=reg_params, fit_intercept=True, cv=5), True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "Overall we see that clustering by neighborhood performs very similarly to the original dataset with zipcodes. We will next try looking at Median Absolute Error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def median_absolute_errors(x, y, log_bool=None):\n",
    "    reg_params = 10.**np.linspace(-10, 5, 10)\n",
    "    models = [ linear_model.Ridge(), RidgeCV(alphas=reg_params, cv=5), linear_model.Lasso(), LassoCV(alphas=reg_params, cv=5), linear_model.ElasticNet(), linear_model.BayesianRidge(), linear_model.OrthogonalMatchingPursuit() ]\n",
    "    model_labels = np.array(['Ridge', 'RidgeCV', 'Lasso', 'LassoCV', 'ElasticNet', 'BayesRidge', 'OMP'])\n",
    "    model_errors = np.array([])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.35, random_state=15)\n",
    "\n",
    "    for model in models:\n",
    "        model.fit(X_train, y_train)\n",
    "        if not log_bool:\n",
    "            model_err = metrics.median_absolute_error((y_test), model.predict(X_test))\n",
    "            model_errors = np.append(model_errors, model_err)\n",
    "        else:\n",
    "            model_err = metrics.median_absolute_error(np.exp(y_test), np.exp(model.predict(X_test)))\n",
    "            model_errors = np.append(model_errors, model_err)\n",
    "    \n",
    "    model_position = np.arange(model_errors.shape[0])\n",
    "    models_sorted = np.argsort(model_errors)\n",
    "    for i, model in enumerate(model_labels):\n",
    "        print 'Model {} Results: {}'.format(model_labels[i], model_errors[i])\n",
    "\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.bar(model_position, model_errors[models_sorted], align='center')\n",
    "    plt.xticks(model_position, model_labels[models_sorted])\n",
    "    plt.xlabel('Estimator')\n",
    "    plt.ylabel('Median Absolute Error')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median Absolute Error Untransformed Response\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Ridge Results: 45.0733616865\n",
      "Model RidgeCV Results: 44.041747509\n",
      "Model Lasso Results: 41.1483774137\n",
      "Model LassoCV Results: 41.1483774137\n",
      "Model ElasticNet Results: 44.4211434345\n",
      "Model BayesRidge Results: 44.8814025582\n",
      "Model OMP Results: 44.1673181849\n"
     ]
    },
    {
     "data": {
      "image/png": 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kkdD1g919q6VXAgCwwhYZ0/WBqvqppVcCALDCFunpul6m4PXFJN/INK5rV3dfe6mVAQCs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\nD6fTXD6rjysmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8e1bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_absolute_errors(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median Absolute Error Log-Transformed Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Ridge Results: 25.3664157775\n",
      "Model RidgeCV Results: 25.3664157775\n",
      "Model Lasso Results: 53.9575620514\n",
      "Model LassoCV Results: 25.3705859359\n",
      "Model ElasticNet Results: 53.9575620514\n",
      "Model BayesRidge Results: 25.3482809254\n",
      "Model OMP Results: 27.7305304401\n"
     ]
    },
    {
     "data": {
      "image/png": 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BV7ZWkSRJ0gDqZSA9ABGxG3AsJXD9FPhsW0VJkiQNmnFDV0Q8DngFcBywK+VM9Ic2l/CR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\nXZIkSRUYuiRJkir4/1EWamF5ZOfXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abac1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_absolute_errors(x, y_log, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Listing House Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_single = data[data['beds'] == min(data['beds'])]\n",
    "\n",
    "# split into x_1 and y_1 (note that we do not include id and host_id as predictors)\n",
    "x_1 = data_single.iloc[:, 2:-2]\n",
    "y_1 = data_single.iloc[:, -2]\n",
    "y_1_log = data_single.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Ridge Results: 25.913234694\n",
      "Model RidgeCV Results: 25.913234694\n",
      "Model Lasso Results: 26.1649614946\n",
      "Model LassoCV Results: 25.9530509914\n",
      "Model ElasticNet Results: 36.1908957651\n",
      "Model BayesRidge Results: 25.5724623328\n",
      "Model OMP Results: 26.2812380006\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJM0w3Rxe/ALwAPBSYHvg74Dju1l4RGxNOcXEFzPzTMpYrjHzgN9MqVpJkqQZqpuB9I/K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\nSVIFhi5JkqQKDF2SJEkV/A9g2KX9aLOCOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6864d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_absolute_errors(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Ridge Results: 19.9339124308\n",
      "Model RidgeCV Results: 19.9339124308\n",
      "Model Lasso Results: 39.946048994\n",
      "Model LassoCV Results: 19.9034017465\n",
      "Model ElasticNet Results: 39.946048994\n",
      "Model BayesRidge Results: 19.9287597433\n",
      "Model OMP Results: 22.2644248675\n"
     ]
    },
    {
     "data": {
      "image/png": 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I6yNis8z8SUPLK+35tgSIiLOpwuRxo0zTKxpd94hYBfgh1QfRzfVjXwCOBb4K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\nAgxdkiRJBfwP4qVtluIMJxwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa45610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_absolute_errors(x_1, y_1_log, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "For both the listing data and single house listing data, we see that clustering by neighborhoods performs very similarly. We can speculate that perhaps the model benefitted from the granularity of segmenting by zipcode as versed to grouping them into overlapping neighborhoods."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
